rebased on master

eval_model_cifake.py

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+import os
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torchvision import datasets, models
+import torch.nn as nn
+import torch.optim as optim
+from tqdm import tqdm # https://tqdm.github.io/
+
+# https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+# ======= Settings =======
+DATA_DIR = "./CIFAKE"
+BATCH_SIZE = 16
+NUM_CLASSES = 2
+NUM_EPOCHS = 10
+LEARNING_RATE = 1e-4
+DEVICE = "cpu"
+
+# ======= Transforms =======
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Ensure fixed input size
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],  # ImageNet mean
+                         [0.229, 0.224, 0.225])  # ImageNet std
+])
+
+# ======= Load Data =======
+test_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, "test"), transform=transform)
+test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+
+# ======= Load Model =======
+model = models.resnet18(pretrained=True)
+model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+state = torch.load("resnet18_cifake.pth", map_location=torch.device('cpu'))
+model.load_state_dict(state)
+model = model.to(DEVICE)
+
+
+# ======= Evaluation =======
+model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in tqdm(test_loader):
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        #print(f"total={total}, correct={correct}")
+        if total >= 400:
+            break
+
+print(f"Test Accuracy: {100 * correct / total:.2f}%")
+